Artificial Intelligence Model for Flowable Concrete Mixtures Used in Underwater Construction and Repair

This study explores using artificial neural networks to predict the rheological and mechanical properties of underwater concrete (UWC) mixtures and to evaluate the sensitivity of such properties to variations in mixture ingredients. Artificial neural networks (ANN) mimic the structure and operation of biological neurons and have the unique ability of self-learning, mapping, and functional approximation. Details of the development of the proposed neural network model, its architecture, training, and validation are presented in this study. A database incorporating 175 UWC mixtures from nine different studies was developed to train and test the ANN model. The data are arranged in a patterned format. Each pattern contains an input vector that includes quantity values of the mixture variables influencing the behavior of UWC mixtures (that is, cement, silica fume, fly ash, slag, water, coarse and fine aggregates, and chemical admixtures) and a corresponding output vector that includes the rheological or mechanical property to be modeled. Results show that the ANN model thus developed is not only capable of accurately predicting the slump, slump-flow, washout resistance, and compressive strength of underwater concrete mixtures used in the training process, but it can also effectively predict the aforementioned properties for new mixtures designed within the practical range of the input parameters used in the training process with an absolute error of 4.6, 10.6, 10.6, and 4.4%, respectively.

3D Morphable Model Construction for Robust Ear and Face Recognition

Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online.

A multi-output two-stage locally regularized model construction method using the extreme learning machine

This paper investigates the construction of linear-in-the-parameters (LITP) models for multi-output regression problems. Most existing stepwise forward algorithms choose the regressor terms one by one, each time maximizing the model error reduction ratio. The drawback is that such procedures cannot guarantee a sparse model, especially under highly noisy learning conditions. The main objective of this paper is to improve the sparsity and generalization capability of a model for multi-output regression problems, while reducing the computational complexity. This is achieved by proposing a novel multi-output two-stage locally regularized model construction (MTLRMC) method using the extreme learning machine (ELM). In this new algorithm, the nonlinear parameters in each term, such as the width of the Gaussian function and the power of a polynomial term, are firstly determined by the ELM. An initial multi-output LITP model is then generated according to the termination criteria in the first stage. The significance of each selected regressor is checked and the insignificant ones are replaced at the second stage. The proposed method can produce an optimized compact model by using the regularized parameters. Further, to reduce the computational complexity, a proper regression context is used to allow fast implementation of the proposed method. Simulation results confirm the effectiveness of the proposed technique. © 2013 Elsevier B.V.

North Atlantic marine radiocarbon reservoir ages through Heinrich event H4: A new method for marine age model construction

Cooling and sinking of dense saline water in the Norwegian–Greenland Sea is essential for the formation of North Atlantic Deep Water. The convection in the Norwegian–Greenland Sea allows for a northward flow of warm surface water and southward transport of cold saline water. This circulation system is highly sensitive to climate change and has been shown to operate in different modes. In ice cores the last glacial period is characterized by millennial-scale Dansgaard–Oeschger (D–O) events of warm interstadials and cold stadials. Similar millennial-scale variability (linked to D–O events) is evident from oceanic cores, suggesting a strong coupling of the atmospheric and oceanic circulations system. Particularly long-lasting cold stadials correlate with North Atlantic Heinrich events, where icebergs released from the continents caused a spread of meltwater over the northern North Atlantic and Nordic seas. The meltwater layer is believed to have caused a stop or near-stop in the deep convection, leading to cold climate. The spreading of meltwater and changes in oceanic circulation have a large influence on the carbon exchange between atmosphere and the deep ocean and lead to profound changes in the 14C activity of the surface ocean. Here we demonstrate marine 14C reservoir ages (R) of up to c. 2000 years for Heinrich event H4. Our R estimates are based on a new method for age model construction using identified tephra layers and tie-points based on abrupt interstadial warmings.

New Method Based on the UNIFAC–VISCO Model for the Estimation of Ionic Liquids Viscosity Using the Experimental Data Recommended by Mathematical Gnostics

The viscosity of ionic liquids (ILs) has been modeled as a function of temperature and at atmospheric pressure using a new method based on the UNIFAC–VISCO method. This model extends the calculations previously reported by our group (see Zhao et al. J. Chem. Eng. Data 2016, 61, 2160–2169) which used 154 experimental viscosity data points of 25 ionic liquids for regression of a set of binary interaction parameters and ion Vogel–Fulcher–Tammann (VFT) parameters. Discrepancies in the experimental data of the same IL affect the quality of the correlation and thus the development of the predictive method. In this work, mathematical gnostics was used to analyze the experimental data from different sources and recommend one set of reliable data for each IL. These recommended data (totally 819 data points) for 70 ILs were correlated using this model to obtain an extended set of binary interaction parameters and ion VFT parameters, with a regression accuracy of 1.4%. In addition, 966 experimental viscosity data points for 11 binary mixtures of ILs were collected from literature to establish this model. All the binary data consist of 128 training data points used for the optimization of binary interaction parameters and 838 test data points used for the comparison of the pure evaluated values. The relative average absolute deviation (RAAD) for training and test is 2.9% and 3.9%, respectively.

Design, construction and testing of an air-cycle refrigeration system for road transport

The environmental attractions of air-cycle refrigeration are considerable. Following a thermodynamic design analysis, an air-cycle demonstrator plant was constructed within the restricted physical envelope of an existing Thermo King SL200 trailer refrigeration unit. This unique plant operated satisfactorily, delivering sustainable cooling for refrigerated trailers using a completely natural and safe working fluid. The full load capacity of the air-cycle unit at -20 °C was 7,8 kW, 8% greater than the equivalent vapour-cycle unit, but the fuel consumption of the air-cycle plant was excessively high. However, at part load operation the disparity in fuel consumption dropped from approximately 200% to around 80%. The components used in the air-cycle demonstrator were not optimised and considerable potential exists for efficiency improvements, possibly to the point where the air-cycle system could rival the efficiency of the standard vapour-cycle system at part-load operation, which represents the biggest proportion of operating time for most units.

Model atmosphere and kinematical analyses of early-type stars from the Edinburgh-Cape Survey

We present high-resolution spectroscopic observations of 21 B- type stars, selected from the Edinburgh-Cape Blue Object Survey. Model atmosphere analyses confirm that 14 of these stars are young, main-sequence B-type objects with Population I chemical compositions. The remaining seven are found to be evolved objects, including subdwarfs, horizontal branch and post-AGB objects. A kinematical analysis shows that all 14 young main-sequence stars could have formed in the disc and subsequently been ejected into the halo. These results are combined with the analysis of a previous subsample of stars taken from the Survey. Of the complete sample, 31 have been found to be young, main-sequence objects, with formation in the disc, and subsequent ejection into the halo, again being found to be a plausible scenario.

Steam reforming of model compounds and fast pyrolysis bio-oil on supported noble metal catalysts

The production of hydrogen by steam reforming of bio-oils obtained from the fast pyrolysis of biomass requires the development of efficient catalysts able to cope with the complex chemical nature of the reactant. The present work focuses on the use of noble metal-based catalysts for the steam reforming of a few model compounds and that of an actual bio-oil. The steam reforming of the model compounds was investigated in the temperature range 650-950 degrees C over Pt, Pd and Rh supported on alumina and a ceria-zirconia sample. The model compounds used were acetic acid, phenol, acetone and ethanol. The nature of the support appeared to play a significant role in the activity of these catalysts. The use of ceria-zirconia, a redox mixed oxide, lead to higher H-2 yields as compared to the case of the alumina-supported catalysts. The supported Rh and Pt catalysts were the most active for the steam reforming of these compounds, while Pd-based catalysts poorly performed. The activity of the promising Pt and Rh catalysts was also investigated for the steam reforming of a bio-oil obtained from beech wood fast pyrolysis. Temperatures close to, or higher than, 800 degrees C were required to achieve significant conversions to COx and H-2 (e.g., H-2 yields around 70%). The ceria-zirconia materials showed a higher activity than the corresponding alumina samples. A Pt/ceria-zirconia sample used for over 9 h showed essentially constant activity, while extensive carbonaceous deposits were observed on the quartz reactor walls from early time on stream. In the present case, no benefit was observed by adding a small amount of O-2 to the steam/bio-oil feed (autothermal reforming, ATR), probably partly due to the already high concentration of oxygen in the bio-oil composition. (c) 2005 Elsevier B.V. All rights reserved.